1. Field of the Invention
The present invention relates to a data processing system based on the concept of a neural network.
2. Prior Art
The neural network in this kind of data processing system consists of layers of neuron models (neurons) arranged in parallel, as shown in FIG. 1. In a neuron 1, input data DI1, DI2, DI3 . . . DIn are multiplied by weights W1, W2, W3, . . . Wn. Output data DO is a Comparison result between the weighted sum of inputs and a threshold ".theta.". There are various manners in which to compare them. For example, output data DO becomes "1" when the weighted sum is equal to or more than the threshold ".theta.", and the output data DO becomes "0" when the weighted sum is less than the threshold ".theta.".
For the neural network in FIG. 2, 2 combinations can be expressed by patterns of n bits. Therefore, neurons 1 are necessary for a neural network to judge the input data. 16 neurons are necessary for a bit pattern of 4 bits. The number of neurons doubles for every additional bit.
When a neural network is constructed from many layers, the number of neurons increases in response to the increase in layers ("neural layers" hereinafter), and the number of input lines to be connected to neurons, that is, the number of synapses, also increases.
A neural layer is constructed by arranging such neurons in parallel, and layers are connected in series to form a network. There has been no established theory of how to select the number of neurons or layers. If pressed to give an example, one can perform trials when deciding the number of layers to be 3 or deciding the number of neurons to be equal to the numbers of input data, according to Perceptron which is suggested by Rosenblatt. Therefore, there has never been a clear relationship between the data processing to be performed in a neural network and the capacity or construction of the neural network. Accordingly, it has been unknown if a constructed neural network will actually achieve the expected performance until an experimental result is obtained.